A critical challenge in Big Data science is the overall lack of data ahalysis platforms available for transforming Big Data into biological knowledge. To address this challenge, we propose a set of interconnected computational tools capable of organizing and analyzing heterogeneous data to support combined inquiries and to de-convolute complex relationships embedded within large-scale data. We demonstrate its utility with a cardiovascular-centric platform that is easily generalizable to similar efforts in other disciplines. Our Center has designed a federated data architecture of existing resources substantiated by a solid and growing user base, and innovations to elevate functionality. Novel crowdsourcing and text-mining methods will extract the wealth of untapped knowledge embedded in biomedical literature, and novel in-depth proteomics analytical tools will unprecedentedly elucidate dynamic protein features. A key strength of our platform will be the rigorous validation using clinical data from Jackson Heart Study and the Healthy Elderly Active Longevity (HEAL; Wellderly) cohorts. Our proposal includes nine scientific aims that address three main focus areas: (i) we will build a new model platform that amalgamates community-supported Big Data resources, enabling data annotations and collaborative analyses; (ii) we will integrate molecular data with drug and disease information, both structured and unstructured, for knowledge aggregation, and (iii) we will create on-the-cloud analytical and modeling tools to power in-depth protein discoveries. Specifically, we will create a novel distributed query system and cloud-based infrastructure that is capable of providing unified access to multi-omics datasets; we will develop computational and crowdsourcing methods to systematically define relationships between genes, proteins, diseases, and drugs from the literature, emphasizing cardiovascular medicine; we will rally community participation and promote awareness of collaborative research through outreach and educational games; we will create a platform to analyze and visualize multi-scale pathway models of genes, proteins, and metabolites; we will develop tools and algorithms to mechanistically model spatiotemporal protein networks in organelles and to. predict higher physiological phenotypes; and we will correlate individual phenotypes, health histories, and multi-scale molecular profiles to examine cardiovascular disease mechanisms. These tools will be implemented, delivered, and executed on the cloud infrastructure to minimize the computational power required of users.